Triple

T3142123
Position Surface form Disambiguated ID Type / Status
Subject Mount Pico E65673 entity
Predicate nearbySettlement P350 FINISHED
Object Madalena
Madalena is a coastal town on the Azorean island of Pico in Portugal, known as a gateway to Mount Pico and for its wine culture and maritime heritage.
 E332525 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Madalena | Statement: [Mount Pico, nearbySettlement, Madalena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madalena
Context triple: [Mount Pico, nearbySettlement, Madalena]
  • A. Madalena
    Madalena is a neighborhood in the Brazilian city of Recife, known for its urban character and local commerce.
  • B. Maddalena
    Maddalena is the Italian form of the given name Magdalena, traditionally associated with Mary Magdalene in Christian tradition.
  • C. Isabela
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • D. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • E. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Madalena
Triple: [Mount Pico, nearbySettlement, Madalena]
Generated description
Madalena is a coastal town on the Azorean island of Pico in Portugal, known as a gateway to Mount Pico and for its wine culture and maritime heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madalena
Target entity description: Madalena is a coastal town on the Azorean island of Pico in Portugal, known as a gateway to Mount Pico and for its wine culture and maritime heritage.
  • A. Madalena
    Madalena is a neighborhood in the Brazilian city of Recife, known for its urban character and local commerce.
  • B. Maddalena
    Maddalena is the Italian form of the given name Magdalena, traditionally associated with Mary Magdalene in Christian tradition.
  • C. Isabela
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • D. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • E. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada579b07c8190a7b316f499911a2d completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224e9029c8190bd88dbb18b5f71a8 completed March 12, 2026, 2:28 a.m.
NEDg Description generation batch_69b225c419cc8190ac157b5996132d3f completed March 12, 2026, 2:32 a.m.
NED2 Entity disambiguation (via description) batch_69b2264e67748190920fbd2db5355de4 completed March 12, 2026, 2:34 a.m.
Created at: March 8, 2026, 3:05 p.m.